The KITTI Vision Benchmark Suite

This paper explores a pragmatic approach to
multiple object tracking where the main focus is to
associate objects efficiently for online and
realtime applications. To this end, detection
quality is identified as a key factor influencing
tracking performance, where changing the detector
can improve tracking by up to 18.9%. Despite only
using a rudimentary combination of familiar
techniques such as the Kalman Filter and Hungarian
algorithm for the tracking components, this
approach achieves an accuracy comparable to state-
of-the-art online trackers. Furthermore, due to the
simplicity of our tracking method, the tracker
updates at a rate of 260 Hz which is over 20x
faster than other state-of-the-art trackers.

\iou_threshold=0.3

@inproceedings{bewley2016simple,
title={Simple online and realtime tracking},
author={Bewley, Alex and Ge, Zongyuan and Ott,
Lionel and Ramos, Fabio and Upcroft, Ben},
booktitle={2016 IEEE International Conference on
Image Processing (ICIP)},
pages={3464--3468},
year={2016},
organization={IEEE}
}